Python numpy.float_power() Examples

The following are 24 code examples for showing how to use numpy.float_power(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.

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Example 1
Project: ViolenceDetection   Author: JoshuaPiinRueyPan   File: parameters.py    License: Apache License 2.0 6 votes vote down vote up
def _draw_samples(self, size, random_state):
        seed = random_state.randint(0, 10**6, 1)[0]
        samples = self.other_param.draw_samples(size, random_state=ia.new_random_state(seed))

        elementwise = self.elementwise and not isinstance(self.val, Deterministic)

        if elementwise:
            exponents = self.val.draw_samples(size, random_state=ia.new_random_state(seed+1))
        else:
            exponents = self.val.draw_sample(random_state=ia.new_random_state(seed+1))

        # without this we get int results in the case of
        # Power(<int>, <stochastic float param>)
        samples, exponents = both_np_float_if_one_is_float(samples, exponents)
        samples_dtype = samples.dtype

        # float_power requires numpy>=1.12
        #result = np.float_power(samples, exponents)
        # TODO why was float32 type here replaced with complex number
        # formulation?
        result = np.power(samples.astype(np.complex), exponents).real
        if result.dtype != samples_dtype:
            result = result.astype(samples_dtype)

        return result 
Example 2
Project: slates_semisynth_expts   Author: adith387   File: Estimators.py    License: BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up
def estimate(self, query, logged_ranking, new_ranking, logged_value):
        exactMatch=numpy.absolute(new_ranking-logged_ranking).sum() == 0
        currentValue=0.0
        if exactMatch:
            numAllowedDocs=self.loggingPolicy.dataset.docsPerQuery[query]
            validDocs=logged_ranking.size
            invPropensity=None
            if self.loggingPolicy.allowRepetitions:
                invPropensity=numpy.float_power(numAllowedDocs, validDocs)
            else:
                invPropensity=numpy.prod(range(numAllowedDocs+1-validDocs, numAllowedDocs+1), dtype=numpy.float64)
                
            currentValue=logged_value*invPropensity

        self.updateRunningAverage(currentValue)
        return self.runningMean 
Example 3
Project: slates_semisynth_expts   Author: adith387   File: Estimators.py    License: BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up
def estimate(self, query, logged_ranking, new_ranking, logged_value):
        exactMatch=numpy.absolute(new_ranking-logged_ranking).sum() == 0
        currentValue=0.0
        if exactMatch:
            numAllowedDocs=self.loggingPolicy.dataset.docsPerQuery[query]
            validDocs=logged_ranking.size
            invPropensity=None
            if self.loggingPolicy.allowRepetitions:
                invPropensity=numpy.float_power(numAllowedDocs, validDocs)
            else:
                invPropensity=numpy.prod(range(numAllowedDocs+1-validDocs, numAllowedDocs+1), dtype=numpy.float64)
                
            currentValue=logged_value*invPropensity

            self.updateRunningAverage(currentValue)
            denominatorDelta=invPropensity-self.runningDenominatorMean
            self.runningDenominatorMean+=denominatorDelta/self.runningSum
        if self.runningDenominatorMean!=0.0:
            return 1.0*self.runningMean/self.runningDenominatorMean
        else:
            return 0.0 
Example 4
Project: alpha_zero_othello   Author: bhansconnect   File: aiplayer.py    License: MIT License 6 votes vote down vote up
def pick_move(self, game, side):
        possible_moves = game.possible_moves(side)
        if len(possible_moves) == 0:
            possible_moves.append((-1,-1))
        monte_prob = self.monte_carlo(game, side)
        
        if self.train:
            self.temp_state.append((self.preprocess_input(game.board, side), np.divide(monte_prob, np.sum(monte_prob))))
        
        monte_prob = np.float_power(monte_prob, 1/self.tau)
        monte_prob = np.divide(monte_prob, np.sum(monte_prob))
        
        r = random()
        for i, move in enumerate(possible_moves):
            r -= monte_prob[Othello.move_id(move)]
            if r <= 0:
                return move
        return possible_moves[-1] 
Example 5
Project: VSE-C   Author: ExplorerFreda   File: saliency_visualization.py    License: MIT License 5 votes vote down vote up
def plot_saliency(raw_img, image_var, img_embedding_var, caption_var):
    dis = (caption_var.squeeze() * img_embedding_var.squeeze()).sum()
    dis.backward(retain_graph=True)

    grad = image_var.grad.data.cpu().squeeze().numpy().transpose((1, 2, 0))
    grad = normalize_grad(grad, stat=True)
    grad = imresize((grad * 255).astype('uint8'), (raw_img.height, raw_img.width)) / 255
    grad = normalize_grad(grad.mean(axis=-1, keepdims=True).repeat(3, axis=-1))
    grad = np.float_power(grad, args.grad_power)

    np_img = np.array(raw_img)
    masked_img = np_img * grad
    final = np.hstack([np_img, masked_img.astype('uint8'), (grad * 255).astype('uint8')])
    return Image.fromarray(final.astype('uint8')) 
Example 6
Project: NeuroKit   Author: neuropsychology   File: fractal_dfa.py    License: MIT License 5 votes vote down vote up
def _fractal_dfa_fluctuation(segments, trends, multifractal=False, q=2):

    detrended = segments - trends

    if multifractal is True:
        var = np.var(detrended, axis=1)
        fluctuation = np.float_power(np.mean(np.float_power(var, q / 2), axis=1) / 2, 1 / q.T)
        fluctuation = np.mean(fluctuation)  # Average over qs (not sure of that!)

    else:
        # Compute Root Mean Square (RMS)
        fluctuation = np.sum(detrended ** 2, axis=1) / detrended.shape[1]
        fluctuation = np.sqrt(np.sum(fluctuation) / len(fluctuation))

    return fluctuation 
Example 7
Project: NeuroKit   Author: neuropsychology   File: fractal_dfa.py    License: MIT License 5 votes vote down vote up
def _fractal_mfdfa_q(q=2):
    # TODO: Add log calculator for q ≈ 0

    # Fractal powers as floats
    q = np.asarray_chkfinite(q, dtype=np.float)

    # Ensure q≈0 is removed, since it does not converge. Limit set at |q| < 0.1
    q = q[(q < -0.1) + (q > 0.1)]

    # Reshape q to perform np.float_power
    q = q.reshape(-1, 1)
    return q 
Example 8
Project: recruit   Author: Frank-qlu   File: test_umath.py    License: Apache License 2.0 5 votes vote down vote up
def test_type_conversion(self):
        arg_type = '?bhilBHILefdgFDG'
        res_type = 'ddddddddddddgDDG'
        for dtin, dtout in zip(arg_type, res_type):
            msg = "dtin: %s, dtout: %s" % (dtin, dtout)
            arg = np.ones(1, dtype=dtin)
            res = np.float_power(arg, arg)
            assert_(res.dtype.name == np.dtype(dtout).name, msg) 
Example 9
Project: lambda-packs   Author: ryfeus   File: _continuous_distns.py    License: MIT License 5 votes vote down vote up
def _argcheck(self, h, k):
        condlist = [np.logical_and(h > 0, k > 0),
                    np.logical_and(h > 0, k == 0),
                    np.logical_and(h > 0, k < 0),
                    np.logical_and(h <= 0, k > 0),
                    np.logical_and(h <= 0, k == 0),
                    np.logical_and(h <= 0, k < 0)]

        def f0(h, k):
            return (1.0 - float_power(h, -k))/k

        def f1(h, k):
            return np.log(h)

        def f3(h, k):
            a = np.empty(np.shape(h))
            a[:] = -np.inf
            return a

        def f5(h, k):
            return 1.0/k

        self.a = _lazyselect(condlist,
                             [f0, f1, f0, f3, f3, f5],
                             [h, k],
                             default=np.nan)

        def f0(h, k):
            return 1.0/k

        def f1(h, k):
            a = np.empty(np.shape(h))
            a[:] = np.inf
            return a

        self.b = _lazyselect(condlist,
                             [f0, f1, f1, f0, f1, f1],
                             [h, k],
                             default=np.nan)
        return h == h 
Example 10
Project: vnpy_crypto   Author: birforce   File: test_umath.py    License: MIT License 5 votes vote down vote up
def test_type_conversion(self):
        arg_type = '?bhilBHILefdgFDG'
        res_type = 'ddddddddddddgDDG'
        for dtin, dtout in zip(arg_type, res_type):
            msg = "dtin: %s, dtout: %s" % (dtin, dtout)
            arg = np.ones(1, dtype=dtin)
            res = np.float_power(arg, arg)
            assert_(res.dtype.name == np.dtype(dtout).name, msg) 
Example 11
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_umath.py    License: MIT License 5 votes vote down vote up
def test_type_conversion(self):
        arg_type = '?bhilBHILefdgFDG'
        res_type = 'ddddddddddddgDDG'
        for dtin, dtout in zip(arg_type, res_type):
            msg = "dtin: %s, dtout: %s" % (dtin, dtout)
            arg = np.ones(1, dtype=dtin)
            res = np.float_power(arg, arg)
            assert_(res.dtype.name == np.dtype(dtout).name, msg) 
Example 12
Project: trax   Author: google   File: math_ops.py    License: Apache License 2.0 5 votes vote down vote up
def float_power(x1, x2):
  return power(x1, x2) 
Example 13
Project: GraphicDesignPatternByPython   Author: Relph1119   File: test_umath.py    License: MIT License 5 votes vote down vote up
def test_type_conversion(self):
        arg_type = '?bhilBHILefdgFDG'
        res_type = 'ddddddddddddgDDG'
        for dtin, dtout in zip(arg_type, res_type):
            msg = "dtin: %s, dtout: %s" % (dtin, dtout)
            arg = np.ones(1, dtype=dtin)
            res = np.float_power(arg, arg)
            assert_(res.dtype.name == np.dtype(dtout).name, msg) 
Example 14
Project: GraphicDesignPatternByPython   Author: Relph1119   File: _continuous_distns.py    License: MIT License 5 votes vote down vote up
def _argcheck(self, h, k):
        condlist = [np.logical_and(h > 0, k > 0),
                    np.logical_and(h > 0, k == 0),
                    np.logical_and(h > 0, k < 0),
                    np.logical_and(h <= 0, k > 0),
                    np.logical_and(h <= 0, k == 0),
                    np.logical_and(h <= 0, k < 0)]

        def f0(h, k):
            return (1.0 - float_power(h, -k))/k

        def f1(h, k):
            return np.log(h)

        def f3(h, k):
            a = np.empty(np.shape(h))
            a[:] = -np.inf
            return a

        def f5(h, k):
            return 1.0/k

        self.a = _lazyselect(condlist,
                             [f0, f1, f0, f3, f3, f5],
                             [h, k],
                             default=np.nan)

        def f0(h, k):
            return 1.0/k

        def f1(h, k):
            a = np.empty(np.shape(h))
            a[:] = np.inf
            return a

        self.b = _lazyselect(condlist,
                             [f0, f1, f1, f0, f1, f1],
                             [h, k],
                             default=np.nan)
        return h == h 
Example 15
Project: imgaug   Author: aleju   File: parameters.py    License: MIT License 5 votes vote down vote up
def _draw_samples(self, size, random_state):
        rngs = random_state.duplicate(2)
        samples = self.other_param.draw_samples(size, random_state=rngs[0])

        elementwise = (
            self.elementwise
            and not isinstance(self.val, Deterministic))

        if elementwise:
            exponents = self.val.draw_samples(size, random_state=rngs[1])
        else:
            exponents = self.val.draw_sample(random_state=rngs[1])

        # without this we get int results in the case of
        # Power(<int>, <stochastic float param>)
        samples, exponents = both_np_float_if_one_is_float(samples, exponents)
        samples_dtype = samples.dtype

        # TODO switch to this as numpy>=1.15 is now a requirement
        #      float_power requires numpy>=1.12
        # result = np.float_power(samples, exponents)
        # TODO why was float32 type here replaced with complex number
        #      formulation?
        result = np.power(samples.astype(np.complex), exponents).real
        if result.dtype != samples_dtype:
            result = result.astype(samples_dtype)

        return result 
Example 16
Project: DL.EyeSight   Author: liuguiyangnwpu   File: parameter.py    License: GNU General Public License v3.0 5 votes vote down vote up
def _draw_samples(self, size, random_state):
        seed = random_state.randint(0, 10**6, 1)[0]
        samples = self.other_param.draw_samples(
            size,
            random_state=eu.new_random_state(seed))

        elementwise = self.elementwise and not isinstance(self.val, Deterministic)

        if elementwise:
            exponents = self.val.draw_samples(
                size,
                random_state=eu.new_random_state(seed+1))
        else:
            exponents = self.val.draw_sample(
                random_state=eu.new_random_state(seed+1))

        # without this we get int results in the case of
        # Power(<int>, <stochastic float param>)
        samples, exponents = both_np_float_if_one_is_float(samples, exponents)
        samples_dtype = samples.dtype

        # float_power requires numpy>=1.12
        #result = np.float_power(samples, exponents)
        # TODO why was float32 type here replaced with complex number
        # formulation?
        result = np.power(samples.astype(np.complex), exponents).real
        if result.dtype != samples_dtype:
            result = result.astype(samples_dtype)

        return result 
Example 17
def test_type_conversion(self):
        arg_type = '?bhilBHILefdgFDG'
        res_type = 'ddddddddddddgDDG'
        for dtin, dtout in zip(arg_type, res_type):
            msg = "dtin: %s, dtout: %s" % (dtin, dtout)
            arg = np.ones(1, dtype=dtin)
            res = np.float_power(arg, arg)
            assert_(res.dtype.name == np.dtype(dtout).name, msg) 
Example 18
Project: pySINDy   Author: luckystarufo   File: test_umath.py    License: MIT License 5 votes vote down vote up
def test_type_conversion(self):
        arg_type = '?bhilBHILefdgFDG'
        res_type = 'ddddddddddddgDDG'
        for dtin, dtout in zip(arg_type, res_type):
            msg = "dtin: %s, dtout: %s" % (dtin, dtout)
            arg = np.ones(1, dtype=dtin)
            res = np.float_power(arg, arg)
            assert_(res.dtype.name == np.dtype(dtout).name, msg) 
Example 19
Project: mxnet-lambda   Author: awslabs   File: test_umath.py    License: Apache License 2.0 5 votes vote down vote up
def test_type_conversion(self):
        arg_type = '?bhilBHILefdgFDG'
        res_type = 'ddddddddddddgDDG'
        for dtin, dtout in zip(arg_type, res_type):
            msg = "dtin: %s, dtout: %s" % (dtin, dtout)
            arg = np.ones(1, dtype=dtin)
            res = np.float_power(arg, arg)
            assert_(res.dtype.name == np.dtype(dtout).name, msg) 
Example 20
Project: Splunking-Crime   Author: nccgroup   File: _continuous_distns.py    License: GNU Affero General Public License v3.0 5 votes vote down vote up
def _argcheck(self, h, k):
        condlist = [np.logical_and(h > 0, k > 0),
                    np.logical_and(h > 0, k == 0),
                    np.logical_and(h > 0, k < 0),
                    np.logical_and(h <= 0, k > 0),
                    np.logical_and(h <= 0, k == 0),
                    np.logical_and(h <= 0, k < 0)]

        def f0(h, k):
            return (1.0 - float_power(h, -k))/k

        def f1(h, k):
            return np.log(h)

        def f3(h, k):
            a = np.empty(np.shape(h))
            a[:] = -np.inf
            return a

        def f5(h, k):
            return 1.0/k

        self.a = _lazyselect(condlist,
                             [f0, f1, f0, f3, f3, f5],
                             [h, k],
                             default=np.nan)

        def f0(h, k):
            return 1.0/k

        def f1(h, k):
            a = np.empty(np.shape(h))
            a[:] = np.inf
            return a

        self.b = _lazyselect(condlist,
                             [f0, f1, f1, f0, f1, f1],
                             [h, k],
                             default=np.nan)
        return h == h 
Example 21
Project: elasticintel   Author: securityclippy   File: test_umath.py    License: GNU General Public License v3.0 5 votes vote down vote up
def test_type_conversion(self):
        arg_type = '?bhilBHILefdgFDG'
        res_type = 'ddddddddddddgDDG'
        for dtin, dtout in zip(arg_type, res_type):
            msg = "dtin: %s, dtout: %s" % (dtin, dtout)
            arg = np.ones(1, dtype=dtin)
            res = np.float_power(arg, arg)
            assert_(res.dtype.name == np.dtype(dtout).name, msg) 
Example 22
Project: coffeegrindsize   Author: jgagneastro   File: test_umath.py    License: MIT License 5 votes vote down vote up
def test_type_conversion(self):
        arg_type = '?bhilBHILefdgFDG'
        res_type = 'ddddddddddddgDDG'
        for dtin, dtout in zip(arg_type, res_type):
            msg = "dtin: %s, dtout: %s" % (dtin, dtout)
            arg = np.ones(1, dtype=dtin)
            res = np.float_power(arg, arg)
            assert_(res.dtype.name == np.dtype(dtout).name, msg) 
Example 23
Project: Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda   Author: PacktPublishing   File: test_umath.py    License: MIT License 5 votes vote down vote up
def test_type_conversion(self):
        arg_type = '?bhilBHILefdgFDG'
        res_type = 'ddddddddddddgDDG'
        for dtin, dtout in zip(arg_type, res_type):
            msg = "dtin: %s, dtout: %s" % (dtin, dtout)
            arg = np.ones(1, dtype=dtin)
            res = np.float_power(arg, arg)
            assert_(res.dtype.name == np.dtype(dtout).name, msg) 
Example 24
Project: twitter-stock-recommendation   Author: alvarobartt   File: test_umath.py    License: MIT License 5 votes vote down vote up
def test_type_conversion(self):
        arg_type = '?bhilBHILefdgFDG'
        res_type = 'ddddddddddddgDDG'
        for dtin, dtout in zip(arg_type, res_type):
            msg = "dtin: %s, dtout: %s" % (dtin, dtout)
            arg = np.ones(1, dtype=dtin)
            res = np.float_power(arg, arg)
            assert_(res.dtype.name == np.dtype(dtout).name, msg)